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2026/27

AI and Computer Vision, Application in Healthcare

UFMFEV-30-M

30 Credits

Academic level: 7

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28 Sep 2026 (Available)

Apply by: 14 Sep 2026

AI, Computer Vision and Applications in Healthcare 28/09/26 UFMFEV-30-M

On campus - Frenchay

Attendance dates: 28 Sep 2026 (Frenchay Campus - room 0T134), 05 Oct 2026 (Frenchay Campus - room 0T134), 12 Oct 2026 (Frenchay Campus - room 0T134), 19 Oct 2026 (Frenchay Campus - room 0T134), 26 Oct 2026 (Frenchay Campus - room 0T134), 02 Nov 2026 (Frenchay Campus - room 0T134), 09 Nov 2026 (Frenchay Campus - room 0T134), 16 Nov 2026 (Frenchay Campus - room 0T134), 23 Nov 2026 (Frenchay Campus - room 0T134), 30 Nov 2026 (Frenchay Campus - room 0T134), 07 Dec 2026 (Frenchay Campus - room 0T134), 14 Dec 2026 (Frenchay Campus - room 0T134), 25 Jan 2027 (Frenchay Campus - room 0T134), 01 Feb 2027 (Frenchay Campus - room 0T134), 08 Feb 2027 (Frenchay Campus - room 0T134), 15 Feb 2027 (Frenchay Campus - room 0T134), 22 Feb 2027 (Frenchay Campus - room 0T134), 01 Mar 2027 (Frenchay Campus - room 0T134), 08 Mar 2027 (Frenchay Campus - room 0T134), 15 Mar 2027 (Frenchay Campus - room 0T134), 05 Apr 2027 (Frenchay Campus - room 0T134), 12 Apr 2027 (Frenchay Campus - room 0T134), 19 Apr 2027 (Frenchay Campus - room 0T134), 26 Apr 2027 (Frenchay Campus - room 0T134)

Course overview

The 30 credit module, AI and Computer Vision, Application in Healthcare, aims to provide the platform to introduce data analytics and programming that will enable you to understand simple machine learning and informatics, as well as the broad applications and implications of AI and computer vision in healthcare.


On successful completion of this level 7 (Masters level) module, you will be able to:


  • critically discriminate between key concepts in the fields of AI, computer vision, autonomous systems, big data, and informatics. 
  • appraise common informatics tools and approaches, in general and for specific healthcare applications. 
  • critically evaluate existing implementations of AI, computer vision and informatics. 
  • demonstrate ethical and professional values with respect to the development of innovative technologies in Health Tech.  


Careers / Further study

This module can contribute towards the MSc Health Technology.


Extra information

If the course you are applying for is fully online or blended learning, please note that you are expected to provide your own headsets/microphones.


For further information


Email: pd@uwe.ac.uk

Telephone: +44 (0)117 32 81158


Content

The course syllabus typically includes:


  • Introduction to AI, data analytics and their application to healthcare
  • Mathematics and computer programming fundamentals for data science
  • Simple computer vision and machine learning
  • Health informatics and big data frameworks
  • Medical imaging
  • The future of computer vision and machine learning in healthcare
  • Defining cyber security.  


Learning and Teaching

The module content will be delivered through a combination of lectures, tutorials and interactive practical classes.


Assessment

Assessment for this module comprises:


  • a written assignment - data analysis report - (2,500 words maximum) where you will summarise the practical work carried in one or more of the 'sprints', reporting to a hypothetical product owner and reflecting on the process for the sake of continuous improvement within their hypothetical organisation.  
  • a presentation - viva voce examination - which will focus on the topics of the written work, providing further opportunity for you to demonstrate your independent achievement of the learning outcomes in a different format. 


Accredited by

Why choose UWE Bristol?

UWE Bristol works closely with employers and industry partners, ensuring that course content reflects today’s challenges, emerging trends, and the skills that organisations are actively seeking.

Many courses are offered part-time, online, or in blended formats, making it easier to balance learning with work and personal commitments without compromising depth or quality.

Teaching staff often come from professional backgrounds, bringing practical insight, case studies, and hands-on expertise that help learners apply knowledge directly to their careers.

Learners benefit from career guidance, mentoring, and access to a vibrant network of professionals, alumni, and industry events that can open doors to new roles or progression.

UWE Bristol is known for its emphasis on practical skills, innovation, and employability, giving learners added confidence that their qualification will be respected and valuable in the workplace.

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Course details

Course leader

David Western

Course delivery

On campus - Frenchay

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Prerequisites

Course fees

Supplementary fee information


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